Information Theory (cs.IT)
Fri, 01 Sep 2023
1.Self-Sustainable Key Generation: Strategies and Performance Bounds under DoS Attacks
Authors:Rusni Kima Mangang, J. Harshan
Abstract: Denial-of-Service (DoS) threats pose a major challenge to the idea of physical-layer key generation as the underlying wireless channels for key extraction are usually public. Identifying this vulnerability, we study the effect of DoS threats on relay-assisted key generation, and show that a reactive jamming attack on the distribution phase of relay-assisted key generation can forbid the nodes from extracting secret keys. To circumvent this problem, we propose a self-sustainable key generation model, wherein a frequency-hopping based distribution phase is employed to evade the jamming attack even though the participating nodes do not share prior credentials. A salient feature of the idea is to carve out a few bits from the key generation phase and subsequently use them to pick a frequency band at random for the broadcast phase. Interesting resource-allocation problems are formulated on how to extract maximum number of secret bits while also being able to evade the jamming attack with high probability. Tractable low-complexity solutions are also provided to the resource-allocation problems, along with insights on the feasibility of their implementation in practice.
2.Achievable Rate Region and Path-Based Beamforming for Multi-User Single-Carrier Delay Alignment Modulation
Authors:Xingwei Wang, Haiquan Lu, Yong Zeng, Xiaoli Xu, Jie Xu
Abstract: Delay alignment modulation (DAM) is a novel wideband transmission technique for mmWave massive MIMO systems, which exploits the high spatial resolution and multi-path sparsity to mitigate ISI, without relying on channel equalization or multi-carrier transmission. In particular, DAM leverages the delay pre-compensation and path-based beamforming to effectively align the multi-path components, thus achieving the constructive multi-path combination for eliminating the ISI while preserving the multi-path power gain. Different from the existing works only considering single-user DAM, this paper investigates the DAM technique for multi-user mmWave massive MIMO communication. First, we consider the asymptotic regime when the number of antennas Mt at BS is sufficiently large. It is shown that by employing the simple delay pre-compensation and per-path-based MRT beamforming, the single-carrier DAM is able to perfectly eliminate both ISI and IUI. Next, we consider the general scenario with Mt being finite. In this scenario, we characterize the achievable rate region of the multi-user DAM system by finding its Pareto boundary. Specifically, we formulate a rate-profile-constrained sum rate maximization problem by optimizing the per-path-based beamforming. Furthermore, we present three low-complexity per-path-based beamforming strategies based on the MRT, zero-forcing, and regularized zero-forcing principles, respectively, based on which the achievable sum rates are studied. Finally, we provide simulation results to demonstrate the performance of our proposed strategies as compared to two benchmark schemes based on the strongest-path-based beamforming and the prevalent OFDM, respectively. It is shown that DAM achieves higher spectral efficiency and/or lower peak-to-average-ratio, for systems with high spatial resolution and multi-path diversity.
3.Deep Joint Source-Channel Coding for Adaptive Image Transmission over MIMO Channels
Authors:Haotian Wu, Yulin Shao, Chenghong Bian, Krystian Mikolajczyk, Deniz Gündüz
Abstract: This paper introduces a vision transformer (ViT)-based deep joint source and channel coding (DeepJSCC) scheme for wireless image transmission over multiple-input multiple-output (MIMO) channels, denoted as DeepJSCC-MIMO. We consider DeepJSCC-MIMO for adaptive image transmission in both open-loop and closed-loop MIMO systems. The novel DeepJSCC-MIMO architecture surpasses the classical separation-based benchmarks with robustness to channel estimation errors and showcases remarkable flexibility in adapting to diverse channel conditions and antenna numbers without requiring retraining. Specifically, by harnessing the self-attention mechanism of ViT, DeepJSCC-MIMO intelligently learns feature mapping and power allocation strategies tailored to the unique characteristics of the source image and prevailing channel conditions. Extensive numerical experiments validate the significant improvements in transmission quality achieved by DeepJSCC-MIMO for both open-loop and closed-loop MIMO systems across a wide range of scenarios. Moreover, DeepJSCC-MIMO exhibits robustness to varying channel conditions, channel estimation errors, and different antenna numbers, making it an appealing solution for emerging semantic communication systems.